Commit 5b25ec91 authored by Francesco Bailo's avatar Francesco Bailo
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Init

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## Download your files
To download you files click [here](). The file is a compressed folder. Make sure you expand it.
## Connect to a file
1. Click on "Connect to Data".
2. Then, "To a File" -> "Text file".
This two step will connect your workbook to a .csv text file. But you also connect to an Excel file, a PDF or a spatial file.
3. Locate the file `ex01_gender.csv` and open it.
4. To connect to an additional file, next to "Connections" click on "Add" (and then redo steps 2. and 3.)
## Join two tables
Sometimes, your data is scattered in more than just one table. In this example, for each observation (i.e. person) we have `Gender` in one table and the `Weight` and `Height` in a different table. To *join* the information from the two tables, we need a column with a unique ID. In this example, that column is labelled, not surprisingly, `id`.
After you connect to your files, Tableau will automagically join two or more table if it identifies a common unique ID column. You can check on which columns the tables have been joined (or change the join columns) by clicking on the symbol connecting the two tables.
![Tableau join](img/join.png)
[For more details, see the Tableau documentation](https://help.tableau.com/current/pro/desktop/en-us/joining_tables.htm)
## Visualise your data
### Visualise the count
If you want to count how many females and males you have in your data,
1. Go to your worksheet (probably called "Sheet 1").
You should now see you data on the left with a list of your "Dimensions" and "Measures".
2. Drag-and-drop `Gender` in the "Columns" bar.
3. Drag-and-drop `Gender` in the "Rows" bar.
4. Click on the little triangle on the right of "Gender" in the "Rows" bar.
5. Select "Measure" -> "Count".
Not that exciting, we have 5k records for each `Gender`.
### Visualise the average
1. Remove the "CNT(Gender)" from the "Rows" bar by drag-and-dropping it somewhere else.
2. Drag-and-drop `Height` (or `Weight`) from below "Measures" in the "Rows" bar.
Automatically, Tableau will sum all the values. So you can see now what is the total height (or weight) calculated by summing all the heights (and weights) in the data. This is not very interesting. Let's instead calculate the average for male and females.
3. Click on the little triangle on the right of "Height" (or "Weight") in the "Rows" bar.
4. Select "Measure" -> "Average".
### Visualise the distribution of your measures
It's always important to have a sense of the distribution of a measure before you start analysing it.
1. Click on `Height` (or `Weight`) from below "Measures", on the left-hand side of the window.
2. Click on the histogram view on the right-hand side of the window.
![Tableau join](img/histogram.png)
3. Drag-and-drop `Gender` in the "Rows" bar.
Do you understan what you see?
### Export your view
To export the visualisation you have created you can
1. Click on "File" in the menu bar, then "Export as PowerPoint..." -> "Export".
## Geographic data
### Connect to the data
* `ex02_gdp-capita.xlsx` (connect to an "Excel file")
* `ex02_NUTS_RG_10M_2016_3857_LEVL_2.shp` (connect to a "Spatial file")
### Join the tables
Tableu will not be able to join the two tables. You will need to do it on your own.
1. Select `geo` on the left side of the "=" sign.
2. Select `NUTS_ID` on the right side of the "=" sign.
3. Close the "Join" windown
Your table should now be joined!
### Change the data type of a column
The column `value` was loaded as a string ("Abc"). We need to consider it as a number.
1. Click on "Abc" in the header of the "value" column.
2. Select "Number (whole)".
### Visualise the geographic data
Let's go to the "Sheet 1" tab.
1. Drag-and-drop `Geometry` from below "Measures" where you read "Drop field here".
You should now see the map of Europe.
Make sure that `value` from "Sheet 1" is below "Measures". If you find it below "Dimensions" you will need to Drag-and-drop it from "Dimensions" to "Measures".
2. Drag-and-drop `value` from below "Measures" to where you read "Drop field here".
### Change the colors
1. Click on "Color".
2. Set the "Opacity" to `100%`.
3. Click on "Edit Colors" to change the colors.
### Filter the values
The contrast between the different regions is not very strong because there is an [outlier](https://en.wikipedia.org/wiki/Outlier). To remove the outlier from the visualisation
1. Drag-and-drop `value` from below "Measures" to where you read "Filters".
2. Click on "Next".
3. Move the slide to somewhere below 100,000.
4. Click "OK".
Now all the values above 100,000 have been removed!
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PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.017453292519943295]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0]]
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